# -*- coding: utf-8 -*-
# @Author: luoling
# @Date:   2019-12-03 17:49:08
# @Last Modified by:   luoling
# @Last Modified time: 2019-12-09 11:21:48

import torch.nn as nn
from torch.nn import functional as F
import torch
# import torch.utils.model_zoo as model_zoo

from inplace_abn import InPlaceABN
import functools
BatchNorm2d = functools.partial(InPlaceABN, activation='identity')

# model_urls = {
#     'resnet101': 'https://pan.baidu.com/s/1NoxI_JetjSVa7uqgVSKdPw#list/path=%2FCE2P/resnet101-imagenet.pth',
# }


def conv3x3(in_planes, out_planes, stride=1):
    "3x3 convolution with padding"
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
                     padding=1, bias=False)


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None, fist_dilation=1, multi_grid=1):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=dilation*multi_grid, dilation=dilation*multi_grid, bias=False)
        self.bn2 = BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=False)
        self.relu_inplace = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.dilation = dilation
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out = out + residual
        out = self.relu_inplace(out)

        return out


class ASPPModule(nn.Module):
    """
    Reference: 
        Chen, Liang-Chieh, et al. *"Rethinking Atrous Convolution for Semantic Image Segmentation."*
    """

    def __init__(self, features, inner_features=256, out_features=512, dilations=(12, 24, 36)):
        super(ASPPModule, self).__init__()

        self.conv1 = nn.Sequential(nn.AdaptiveAvgPool2d((1, 1)),
                                   nn.Conv2d(
                                       features, inner_features, kernel_size=1, padding=0, dilation=1, bias=False),
                                   BatchNorm2d(inner_features))
        self.conv2 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=1, padding=0, dilation=1, bias=False),
                                   BatchNorm2d(inner_features))
        self.conv3 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations[0], dilation=dilations[0], bias=False),
                                   BatchNorm2d(inner_features))
        self.conv4 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations[1], dilation=dilations[1], bias=False),
                                   BatchNorm2d(inner_features))
        self.conv5 = nn.Sequential(nn.Conv2d(features, inner_features, kernel_size=3, padding=dilations[2], dilation=dilations[2], bias=False),
                                   BatchNorm2d(inner_features))

        self.bottleneck = nn.Sequential(
            nn.Conv2d(inner_features * 5, out_features,
                      kernel_size=1, padding=0, dilation=1, bias=False),
            BatchNorm2d(out_features),
            nn.Dropout2d(0.1)
        )

    def forward(self, x):
        _, _, h, w = x.size()
        feat1 = F.interpolate(self.conv1(x), size=(
            h, w), mode='bilinear', align_corners=True)

        feat2 = self.conv2(x)
        feat3 = self.conv3(x)
        feat4 = self.conv4(x)
        feat5 = self.conv5(x)
        out = torch.cat((feat1, feat2, feat3, feat4, feat5), 1)

        bottle = self.bottleneck(out)
        return bottle


class EdgeModule(nn.Module):

    def __init__(self, in_fea=[256, 512, 1024], mid_fea=256, out_fea=2):
        super(EdgeModule, self).__init__()

        self.conv1 = nn.Sequential(
            nn.Conv2d(in_fea[0], mid_fea, kernel_size=1,
                      padding=0, bias=False),
            BatchNorm2d(mid_fea)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(in_fea[1], mid_fea, kernel_size=1,
                      padding=0, bias=False),
            BatchNorm2d(mid_fea)
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(in_fea[2], mid_fea, kernel_size=1,
                      padding=0, bias=False),
            BatchNorm2d(mid_fea)
        )
        self.conv4 = nn.Conv2d(
            mid_fea, out_fea, kernel_size=3, padding=1, bias=True)
        self.conv5 = nn.Conv2d(
            out_fea * 3, out_fea, kernel_size=1, padding=0, bias=True)

    def forward(self, x1, x2, x3):
        _, _, h, w = x1.size()

        edge1_fea = self.conv1(x1)
        edge1 = self.conv4(edge1_fea)
        edge2_fea = self.conv2(x2)
        edge2 = self.conv4(edge2_fea)
        edge3_fea = self.conv3(x3)
        edge3 = self.conv4(edge3_fea)

        edge2_fea = F.interpolate(edge2_fea, size=(
            h, w), mode='bilinear', align_corners=True)
        edge3_fea = F.interpolate(edge3_fea, size=(
            h, w), mode='bilinear', align_corners=True)
        edge2 = F.interpolate(edge2, size=(
            h, w), mode='bilinear', align_corners=True)
        edge3 = F.interpolate(edge3, size=(
            h, w), mode='bilinear', align_corners=True)

        edge = torch.cat([edge1, edge2, edge3], dim=1)
        edge_fea = torch.cat([edge1_fea, edge2_fea, edge3_fea], dim=1)
        edge = self.conv5(edge)

        return edge, edge_fea


class PSPModule(nn.Module):
    """
    Reference: 
        Zhao, Hengshuang, et al. *"Pyramid scene parsing network."*
    """

    def __init__(self, features, out_features=512, sizes=(1, 2, 3, 6)):
        super(PSPModule, self).__init__()

        self.stages = []
        self.stages = nn.ModuleList(
            [self._make_stage(features, out_features, size) for size in sizes])
        self.bottleneck = nn.Sequential(
            nn.Conv2d(features + len(sizes) * out_features, out_features,
                      kernel_size=3, padding=1, bias=False),
            BatchNorm2d(out_features),
        )

    def _make_stage(self, features, out_features, size):
        prior = nn.AdaptiveAvgPool2d(output_size=(size, size))
        conv = nn.Conv2d(features, out_features, kernel_size=1, bias=False)
        bn = BatchNorm2d(out_features)
        return nn.Sequential(prior, conv, bn)

    def forward(self, feats):
        h, w = feats.size(2), feats.size(3)
        priors = [F.interpolate(input=stage(feats), size=(
            h, w), mode='bilinear', align_corners=True) for stage in self.stages] + [feats]
        bottle = self.bottleneck(torch.cat(priors, 1))

        return bottle


class DecoderModule(nn.Module):
    def __init__(self, num_classes):
        super(DecoderModule, self).__init__()
        self.conv1 = nn.Sequential(
            nn.Conv2d(512, 256, kernel_size=1,
                      padding=0, dilation=1, bias=False),
            BatchNorm2d(256)
        )
        self.conv2 = nn.Sequential(
            nn.Conv2d(256, 48, kernel_size=1, stride=1,
                      padding=0, dilation=1, bias=False),
            BatchNorm2d(48)
        )
        self.conv3 = nn.Sequential(
            nn.Conv2d(304, 256, kernel_size=1,
                      padding=0, dilation=1, bias=False),
            BatchNorm2d(256),
            nn.Conv2d(256, 256, kernel_size=1,
                      padding=0, dilation=1, bias=False),
            BatchNorm2d(256)
        )
        self.conv4 = nn.Conv2d(
            256, num_classes, kernel_size=1, padding=0, dilation=1, bias=True)

    def forward(self, xt, xl):
        _, _, h, w = xl.size()

        xt = F.interpolate(self.conv1(xt), size=(
            h, w), mode='bilinear', align_corners=True)
        xl = self.conv2(xl)
        x = torch.cat([xt, xl], dim=1)
        x = self.conv3(x)
        seg = self.conv4(x)
        return seg, x


class ResNet(nn.Module):
    def __init__(self, block, layers, num_classes):
        self.inplanes = 128
        super(ResNet, self).__init__()
        self.conv1 = conv3x3(3, 64, stride=2)
        self.bn1 = BatchNorm2d(64)
        self.relu1 = nn.ReLU(inplace=False)
        self.conv2 = conv3x3(64, 64)
        self.bn2 = BatchNorm2d(64)
        self.relu2 = nn.ReLU(inplace=False)
        self.conv3 = conv3x3(64, 128)
        self.bn3 = BatchNorm2d(128)
        self.relu3 = nn.ReLU(inplace=False)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)

        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(
            block, 512, layers[3], stride=1, dilation=2, multi_grid=(1, 1, 1))

        self.layer5 = PSPModule(2048, 512)

        self.edge_layer = EdgeModule()
        self.layer6 = DecoderModule(num_classes)
        self.layer7 = nn.Sequential(
            nn.Conv2d(1024, 256, kernel_size=1,
                      padding=0, dilation=1, bias=False),
            BatchNorm2d(256),
            nn.Dropout2d(0.1),
            nn.Conv2d(256, num_classes, kernel_size=1,
                      padding=0, dilation=1, bias=True)
        )

    def _make_layer(self, block, planes, blocks, stride=1, dilation=1, multi_grid=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                BatchNorm2d(planes * block.expansion, affine=True))

        layers = []
        def generate_multi_grid(index, grids): return grids[index % len(
            grids)] if isinstance(grids, tuple) else 1
        layers.append(block(self.inplanes, planes, stride, dilation=dilation,
                            downsample=downsample, multi_grid=generate_multi_grid(0, multi_grid)))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, dilation=dilation,
                                multi_grid=generate_multi_grid(i, multi_grid)))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.relu1(self.bn1(self.conv1(x)))
        x = self.relu2(self.bn2(self.conv2(x)))
        x = self.relu3(self.bn3(self.conv3(x)))
        x = self.maxpool(x)
        x2 = self.layer1(x)
        x3 = self.layer2(x2)
        x4 = self.layer3(x3)
        x5 = self.layer4(x4)
        x = self.layer5(x5)
        edge, edge_fea = self.edge_layer(x2, x3, x4)
        seg1, x = self.layer6(x, x2)
        x = torch.cat([x, edge_fea], dim=1)
        seg2 = self.layer7(x)

        return [[seg1, seg2], [edge]]


def CE2P(num_classes=19, url=None, pretrained=True):
    model = ResNet(Bottleneck, [3, 4, 23, 3], num_classes)
    if pretrained:
        saved_state_dict = torch.load(url)
        # saved_state_dict = model_zoo.load_url(model_urls['resnet101'])
        new_params = model.state_dict().copy()
        for i in saved_state_dict:
            i_parts = i.split('.')
            if not i_parts[0] == 'fc':
                new_params['.'.join(i_parts[0:])] = saved_state_dict[i]
        model.load_state_dict(new_params)
    return model
